Abstract
The Internet of Things is the technology that is exploding in the day-to-day life of the home to the large industrial environment. An IoT connects various applications and services via the internet to make the environment contented. The way of communication among the devices leads to network vulnerability with various attacks. To protect from the security vulnerability of the IoT, the Intrusion Detection Systems (IDS) is employed in the network layer. The network packets from the interconnected IoT applications and services are stored in the Linux server on the end nodes. The packets are got from the server using the crawler into the network layer for attack prediction. Thus, the work contains the main objective is to identify and detect the intrusion among the IoT environment based on machine learning (ML) using the benchmark dataset NSL-KDD. The NSL-KDD dataset is pre-processed to sanitize the null values, eliminating the duplicate and unwanted columns. The cleaned dataset is then assessed to construct the novel custom features and basic features for the attack detection, which represent the feature vector. Novel features are constructed to reduce the learning confusion of machine learning algorithm. The feature vector with the novel and basic features is then processed by employing the feature selection strategy LASSO to get the significant features to increase the prediction accuracy. Due to the outperform of ensembled machine learning algorithms, HSDTKNN (Hybrid Stacking Decision Tree with KNN), HSDTSVM (Hybrid Stacking Decision Tree with SVM) and TCB (Tuned CatBoost) are used for classification. Tuned CatBoost (TCB) technique remarkably predicts the attack that occurs among the packets and generates the alarm. The experimental outcomes established the sufficiency of the proposed model to suits the IoT IDS environment with an accuracy rate of 97.8313%, 0.021687 of error rate, 97.1001% of sensitivity, and specificity of 98.7052%, while prediction.
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Jeyanthi, D.V., Indrani, B. (2022). Intrusion Detection System Intensive on Securing IoT Networking Environment Based on Machine Learning Strategy. In: Hemanth, D.J., Pelusi, D., Vuppalapati, C. (eds) Intelligent Data Communication Technologies and Internet of Things. Lecture Notes on Data Engineering and Communications Technologies, vol 101. Springer, Singapore. https://doi.org/10.1007/978-981-16-7610-9_11
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DOI: https://doi.org/10.1007/978-981-16-7610-9_11
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